Building with AI¶

The practitioner track — how to actually build useful AI products. This is where our courses go deep.

Building with AI is one of the core areas in the AI University map of AI. Explore the diagram, then dive into each topic — every subtopic grows into its own deep-dive over time.

flowchart LR
  IDEA[/Use case/] --> PROMPT[Prompt] --> RAG[Add RAG] --> AGENT[Add tools / agent] --> EVAL[Evals] --> SHIP[[Ship]]

Key topics¶

Prompt engineering

Reliable prompting patterns for real applications.
Building RAG systems

Chunking, embeddings, retrieval, and grounding end to end.
Building agents

Tools, memory, control loops, and evaluation for agentic apps.
Fine-tuning & evals

When to fine-tune, and how to build evals you trust.
Shipping to production

Cost, latency, safety, and deploying on the edge.

A pragmatic build order¶

The teams that ship reliable AI features almost always climb the same ladder, stopping as soon as quality is good enough:

flowchart LR
  P[1 · Prompt] --> R[2 · Add RAG] --> T[3 · Add tools / agent] --> F[4 · Fine-tune] --> S[[Ship + monitor]]
  P -.->|often enough| S
  R -.->|often enough| S

Each rung adds capability and cost, latency, and maintenance. Don't build an agent when a prompt would do; don't fine-tune when RAG would do. Complexity should be earned by evidence.

Evals are the moat¶

The single biggest differentiator between a demo and a product is evaluation. Without evals you're flying blind — every prompt tweak is a guess, and you can't tell if a change helped or hurt.

A useful eval set is small, realistic, and versioned: a collection of representative inputs with known-good outcomes (or a rubric), scored automatically on every change. Build it early; it compounds. See Evaluation & Benchmarks for the techniques.

Production checklist¶

Before an AI feature meets real users, pin down:

Cost — tokens per request × traffic. Cache aggressively; use smaller models where you can.
Latency — stream responses; parallelize retrieval and tool calls.
Safety — input/output filtering, and a plan for prompt injection on any untrusted input.
Fallbacks — what happens when the model is wrong, slow, or unavailable.
Observability — log prompts, outputs, and evals so you can debug and improve.

Deploy on the edge

Cloudflare Workers, D1, and Pages — the stack this very site runs on — are a great home for AI apps: low-latency, cheap, and globally distributed.

NLP & Large Language Models · AI Agents & Autonomy · Data & MLOps · Applications & Industry

Learn this properly

Want hands-on training in building with ai? Explore AI University courses and AI School camps for kids.